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Mendeley readers
Attention Score in Context
| Title |
Nonlocal means-based speckle filtering for ultrasound images
|
|---|---|
| Published in |
IEEE Transactions on Image Processing, May 2009
|
| DOI | 10.1109/tip.2009.2024064 |
| Pubmed ID | |
| Authors |
P. Coupe, P. Hellier, C. Kervrann, C. Barillot |
| Abstract |
In image processing, restoration is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. In this paper, an adaptation of the nonlocal (NL)-means filter is proposed for speckle reduction in ultrasound (US) images. Originally developed for additive white Gaussian noise, we propose to use a Bayesian framework to derive a NL-means filter adapted to a relevant ultrasound noise model. Quantitative results on synthetic data show the performances of the proposed method compared to well-established and state-of-the-art methods. Results on real images demonstrate that the proposed method is able to preserve accurately edges and structural details of the image. |
Mendeley readers
The data shown below were compiled from readership statistics for 199 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| China | 2 | 1% |
| Indonesia | 1 | <1% |
| India | 1 | <1% |
| Portugal | 1 | <1% |
| United Kingdom | 1 | <1% |
| Spain | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Unknown | 188 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 61 | 31% |
| Researcher | 35 | 18% |
| Student > Master | 28 | 14% |
| Student > Bachelor | 10 | 5% |
| Professor > Associate Professor | 8 | 4% |
| Other | 22 | 11% |
| Unknown | 35 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 72 | 36% |
| Computer Science | 52 | 26% |
| Physics and Astronomy | 11 | 6% |
| Medicine and Dentistry | 6 | 3% |
| Agricultural and Biological Sciences | 2 | 1% |
| Other | 7 | 4% |
| Unknown | 49 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 February 2023.
All research outputs
#4,192,356
of 25,377,790 outputs
Outputs from IEEE Transactions on Image Processing
#413
of 4,057 outputs
Outputs of similar age
#18,130
of 122,575 outputs
Outputs of similar age from IEEE Transactions on Image Processing
#9
of 49 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,057 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 122,575 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.